Method, apparatus and system for removing motion artifacts from measurements of bodily parameters

ABSTRACT

A method for removing motion artifacts from devices for sensing bodily parameters and apparatus and system for effecting same. The method includes analyzing segments of measured data representing bodily parameters and possibly noise from motion artifacts. Each segment of measured data may correspond to a single light signal transmitted and detected after transmission or reflection through bodily tissue. Each data segment is frequency analyzed to determine up to three candidate peaks for further analysis. Each of the up to three candidate frequencies may be filtered and various parameters associated with each of the up to three candidate frequencies are calculated. The best frequency, if one exists, is determined by arbitrating the candidate frequencies using the calculated parameters according to predefined criteria. If a best frequency is found, a pulse rate and SpO 2  may be output. If a best frequency is not found, other, conventional techniques for calculating pulse rate and SpO 2  may be used. The above method may be applied to red and infrared pulse oximetry signals prior to calculating pulse rate and/or pulsatile blood oxygen concentration. Apparatus and systems disclosed are configured to perform methods disclosed according to the invention.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of application Ser. No. 09/546,260,filed Apr. 10, 2000, now U.S. Pat No. 6,519,486 B1, issued Feb. 11,2003, which is a continuation-in-part of application Ser. No.09/410,991, filed Oct. 1, 1999, now U.S. Pat. No. 6,393,311 B1, issuedMay 21, 2002, titled METHOD, APPARATUS AND SYSTEM FOR REMOVING MOTIONARTIFACTS FROM MEASUREMENTS OF BODILY PARAMETERS, which claims thebenefit of U.S. provisional patent application Ser. No. 60/104,422,filed Oct. 15, 1998, titled METHOD FOR REMOVING MOTION ARTIFACTS FROMDEVICES FOR SENSING BODILY PARAMETERS AND APPARATUS FOR EFFECTING SAME.

TECHNICAL FIELD

This invention relates to the field of signal processing. Moreparticularly, this invention relates to processing measured signals toremove unwanted signal components caused by noise and especially noisecaused by motion artifacts. Even more particularly, the inventionrelates to a method, apparatus and system for removing motion artifactsin the context of a pulse oximeter.

BACKGROUND OF THE INVENTION

The measurement of physiological signals can often be difficult becausethe underlying physiological processes may generate very low levelsignals. Furthermore, interfering noise is inherent in the body and theinterface between the body and sensors of physiological processes.Examples of physiological measurements include: measurement ofelectrocardiogram (ECG) signals based on the electrical depolarizationof the heart muscle, blood pressure, blood oxygen saturation, partialpressure of CO₂, heart rate, respiration rate, and depth of anesthesia.ECG signals, for example, are typically detected by surface electrodesmounted on the chest of a patient. ECG signals are weak at the signalsource (i.e., the heart) and are even weaker at the surface of thechest. Furthermore, electrical interference from the activity of othermuscles (e.g., noise caused by patient breathing, general movement,etc.) causes additional interference with physiological signals such asan ECG. Thus, considerable care must be taken in the design and use ofphysiological processors to enhance the quality of the true signal andreduce the effects of interfering noise signals.

It is convenient to characterize a measured signal as being a compositesignal composed of a true signal component and a noise signal component.The terms “measured signal” and “composite signal” will be usedinterchangeably hereinafter. Signal processors are frequently used toremove noise signal components from a composite measured signal in orderto obtain a signal that closely, if not identically, represents the truesignal. Conventional filtering techniques such as low pass, band pass,and high pass filtering can be used to remove noise signal componentsfrom the measured composite signal where the noise signal componentoccupies a frequency range outside the true signal component. Moresophisticated techniques for conventional noise filtering includemultiple notch filters, which are suitable for use where the noisesignal component exists at multiple, distinct frequencies, all outsidethe true signal frequency band.

However, it is often the case that the frequency spectrum of the trueand noise signal components overlap and that the statistical propertiesof both signal components change with time. More importantly, there aremany cases where little is known about the noise signal component. Insuch cases, conventional filtering techniques may be ineffective inextracting the true signal.

The measurement of oxygen saturation in the blood of a patient is acommon physiological measurement, the accuracy of which may becompromised by the presence of noise. Knowledge of blood oxygensaturation can be critical during surgery. There are means of obtainingblood oxygen saturation by invasive techniques, such as extracting andtesting blood removed from a patient using a co-oximeter. But, suchinvasive means are typically time consuming, expensive, anduncomfortable for the patient. Fortunately, non-invasive measurements ofblood oxygen saturation can be made using known properties of energyattenuation as a selected form of energy passes through a bodily medium.Such non-invasive measurements are performed routinely with a pulseoximeter.

The basic idea behind energy attenuation measurements as employed inpulse oximetry is as follows. Radiant energy is directed toward a bodilymedium, where the medium is derived from or contained within a patient,and the amplitude of the energy transmitted through or reflected fromthe medium is then measured. The amount of attenuation of the incidentenergy caused by the medium is strongly dependent on the thickness andcomposition of the medium through which the energy must pass, as well asthe specific form of energy selected. Information about a physiologicalsystem can be derived from data taken from the attenuated signal of theincident energy transmitted or reflected. However, the accuracy of suchinformation is reduced where the measured signal includes noise.Furthermore, non-invasive measurements often do not afford theopportunity to selectively observe the interference causing the noisesignal component, making it difficult to remove.

A pulse oximeter is one example of a physiological monitoring systemthat is based upon the measurement of energy attenuated by biologicaltissues and substances. More specifically, a pulse oximeter measures thevariable absorption caused by blood volume changes, primarily arterialin origin. Pulse oximeters transmit electromagnetic energy at twodifferent wavelengths, for example at 660 nm (red) and 940 nm (infrared,hereinafter IR) into the tissue and measure the attenuation of theenergy as a function of time. The output signal of a pulse oximeter issensitive to the pulsatile portion of the arterial blood flow andcontains a component that is a waveform representative of the patient'sarterial pulse. This type of signal, which contains a component relatedto the patient's pulse, is called a plethysmographic waveform orplethysmogram.

The period of rhythmic contraction of the heart by which blood is driventhrough the aorta and pulmonary artery is known as systole. Maximumlight absorbance occurs during the systole of a cardiac cycle and isindicated on a plethysmogram by a low point or systolic valley.Conversely, the period of rhythmic relaxation and dilation of the heartcavities occurs during diastole when blood is drawn into the heartcavities. Minimum light absorbance occurs during the diastole of acardiac cycle and is indicated on a plethysmogram by a high point ordiastolic peak.

Pulse oximetry measurements typically use a digit, such as a finger, oran ear lobe or other element of the body, where blood flows close to theskin as the medium through which light energy is transmitted. Thefinger, for example, is composed of various tissues and substancesincluding skin, fat, bone, muscle, blood, etc. The extent to which eachof these biological tissues and substances attenuate incidentelectromagnetic energy is generally known. However, the effect of motioncan cause changes in the optical coupling of the sensor (or probe) tothe finger, the underlying physiology, the local vasculature, opticalproperties of tissues due to changing optical path length as well ascombinations and interactions of all of the above. Thus, patient motionmay cause erratic energy attenuation.

A typical pulse oximeter includes a sensor, cabling from the sensor to acomputer for signal processing and visual display, the computer andvisual display typically being included in a patient monitor. The sensortypically includes two light emitting diodes (LEDs) placed across afinger tip and a photodetector on the side opposite the LEDs. Thedetector measures both transmitted light signals once they have passedthrough the finger. The signals are routed to a computer for analysisand display of the various parameters measured.

The underlying physical basis of a pulse oximeter is Beer's law (alsoreferred to as Beer-Lambert's or Bouguer's law) that describesattenuation of monochromatic light traveling through a uniform mediumthat absorbs light with the equation:

I _(transmitted) =I _(incident) ·e ^(−dca(λ)),  (1)

where I_(transmitted) is the intensity of the light transmitted throughthe uniform medium, I_(incident) is the intensity of incident light, dis the distance light is transmitted through the uniform medium, c isthe concentration of the absorbing substance in the uniform medium,expressed in units of mmol L⁻¹, and α(λ) is the extinction or absorptioncoefficient of the absorbing substance at wavelength λ, expressed inunits of L/(mmol cm). The properties of Beer's law are valid even ifmore than one substance absorbs light in the medium. Each lightabsorbing substance contributes its part to the total absorbance.However, Beer's law does not strictly apply since an LED's output is notmonochromatic and scattering effects do have a significant influence.Thus, manufacturers often utilize an empirically determined lookup tableto map from the ratio of absorbance (or transmittance) at the red and IRfrequencies to a saturation value.

Two LEDs emit narrowband light (i.e., half power bandwidth of typically15 nm) at two different frequency bands, typically red (centered atabout 660 nm) and IR (centered at about 940 nm). The intensity of lighttransmitted through tissue, I_(transmitted), is different for eachwavelength of light emitted by the LEDs. Oxyhemoglobin (oxygenatedblood) tends to absorb IR light, whereas deoxyhemoglobin (deoxygenatedblood) tends to absorb red light. Thus, the absorption of IR lightrelative to the red light increases with oxyhemoglobin. The ratio of theabsorption coefficients can be used to determine the oxygen saturationof the blood.

To estimate pulsatile blood oxygen saturation, SpO₂, a two-soluteconcentration is assumed. A measure of functional blood oxygensaturation level, SpO₂, can be defined as: $\begin{matrix}{{{S\quad p\quad O_{2}} = {100 \cdot \frac{c_{0}}{c_{r} + c_{0}}}},} & (2)\end{matrix}$

where c₀ represents oxyhemoglobin solute concentration, and c_(r)represents reduced or deoxyhemoglobin solute concentration.

Noise signal components in a measured pulse oximetry light signal canoriginate from both AC and DC sources. DC noise signal components may becaused by transmission of electromagnetic energy through tissues ofrelatively constant thickness within the body, e.g., bone, muscle, skin,blood, etc. Such DC noise signal components may be easily removed withconventional filtering techniques. AC noise signal components may occurwhen tissues being measured are perturbed and, thus, change in thicknesswhile a measurement is being made. Such AC noise signal components aredifficult to remove with conventional filtering techniques. Since mostmaterials in and derived from the body are easily compressed, thethickness of such matter changes if the patient moves during anon-invasive physiological measurement. Thus, patient movement can causethe properties of energy attenuation to vary erratically. The erratic orunpredictable nature of motion artifacts induced by noise signalcomponents is a major obstacle in removing them.

Various approaches to removing motion artifacts from measuredphysiological signals, and particularly for use in pulse oximeters, havebeen proposed. U.S. Pat. Nos. 5,482,036, 5,490,505, 5,632,272,5,685,299, 5,769,785 and 6,036,642, all to Diab et al., and U.S. Pat.No. 5,919,134 to Diab, disclose methods and apparatuses for removingmotion artifacts using adaptive noise cancellation techniques. The basicproposition behind these Diab et al. patents is to first generate anoise reference signal from the two measured signals, and then use thenoise reference signal as an input to an adaptive noise canceller alongwith either or both of the measured signals to remove the referencenoise signal from the measured signals, thus approximating the actualparametric signals of interest. These Diab et al. patents appear torequire the use of both measured input signals to generate a noisereference signal. Where the adaptive noise cancellation involves the useof a correlation canceller as disclosed in U.S. Pat. No. 5,482,036,additional problems include significant computational overhead and undercertain circumstances, the correlation canceller will drive the outputsignal to zero.

Another approach to noise artifact elimination is disclosed in U.S. Pat.No. 5,588,427 to Tien. Tien uses fractal dimension analysis to determinethe complexity of waveforms in order to determine the proper value ofthe ratio of true intensities based on signal complexity. The Tienapproach employs a fractal analyzer to determine values for two ratios,α and β, based on the measured time varying intensity of the transmittedred and IR light signals including noise. α is defined as the ratio ofthe time varying true intensity of light transmitted from the red LEDand the time varying true intensity of the light transmitted from the IRLED. β is a similar ratio relating the noise introduced during themeasurement of the light transmitted by the red LED and the noiseintroduced during the measurement of the light transmitted by the IRLED. According to Tien, a fractal analyzer then determines values for αand β and provides (α,β) pairs to a statistical analyzer. Thestatistical analyzer performs analysis of one or more (α,β) pairs todetermine the best value for α, which is then provided to a look-uptable. The look-up table provides a value corresponding to the arterialoxygen saturation in the patient. While the Tien approach appears to bean innovative use of fractal analysis, it also appears to becomputationally complex.

Yet another approach to noise artifact elimination is disclosed in U.S.Pat. Nos. 5,885,213, 5,713,355, 5,555,882 and 5,368,224, all toRichardson et al. The basic proposition behind the Richardson et al.approach is to switch operative frequencies periodically based onevaluating the noise level associated with various possible frequenciesof operation in order to select the frequency of operation that has thelowest associated noise level. It would appear that data measured at anoisy frequency, using the Richardson et al. approach could be invalidor useless for calculating arterial oxygen saturation. Furthermore,Richardson et al. requires a computational overhead to constantlymonitor which frequency of operation provides the least noise.

Another approach to noise artifact elimination is disclosed in U.S. Pat.No. 5,853,364 to Baker, Jr. et al. The Baker, Jr. et al. approach firstcalculates the heart rate of the patient using an adaptive comb filter,power spectrum and pattern matching. Once the heart rate is determined,the oximetry data is adaptively comb filtered so that only energy atinteger multiples of the heart rate are processed. The comb filtereddata and the raw oximetry data are filtered using a Kalman filter toadaptively modify averaging weights and averaging times to attenuatemotion artifact noise. The adaptive filtering of the Baker, Jr. et al.approach appears to add significant computational complexity to solvethe problem of motion artifact rejection.

Still another approach to noise artifact elimination is disclosed inU.S. Pat. No. 5,431,170 to Mathews. Mathews couples a conventional pulseoximeter light transmitter and receiver with a transducer responsive tomovement or vibration of the body. The transducer provides an electricalsignal varying according to the body movements or vibrations, which isrelatively independent of the blood or other fluid flow pulsations.Mathews then provides means for comparing the light signals measuredwith the transducer output and performing adaptive noise cancellation.An apparent disadvantage of the Mathews approach is the need for asecondary sensor to detect motion.

Still yet another approach to noise artifact elimination is disclosed inU.S. Pat. No. 6,002,952 to Diab et al (hereinafter the '952 patent).Diab et al. recognizes the limitations of adaptive noise cancellationand particularly the use of a correlation canceller. The '952 patentdiscloses the use of frequency domain analysis to extract a pulse ratefrom oximetry data. According to the '952 patent, coupling coefficientsrelated to ratios of uncontaminated measurement data and contaminated(noisy) measurement data can be determined from taking the ratios ateach of a series of spectral peaks identified in the frequency domain.The '952 patent further discloses using the coupling coefficients toidentify the presence of noise by calculating the difference between thelargest and smallest ratio lines for all spectral peaks, determiningwhether that difference is greater than a pre-selected threshold andwhether the frequencies associated with the largest and smallestspectral peaks are arbitrarily close or not to each other. Where noiseis detected, a scale factor is used to scrub the measurement data bycontrolling the gain control input of a gain controlled amplifier. Thescale factor is zero in the presence of no noise, and can range up tothe largest ratio line where there is noise and the frequencies are notclose together. However, the signal scrubbing disclosed in the '952patent appears to rely on a very limited measure of noise, i.e., whetherthe difference between the largest and smallest ratio lines is greaterthan a pre-selected threshold and how close the associated frequenciesof largest spectral peak and the smallest spectral peak are relative toone another. It would be preferable to have multiple confidence measuresin a method or system for determining physiological parameters in thepresence of motion artifacts, e.g., a robust pulse oximeter.

Thus, a need in the art exists for a method, apparatus and system toeliminate motion-induced noise artifacts from light signals, that isrelatively simple computationally, and that does not require more thanone sensor, does not use correlation cancellers or adaptive noisecancellation and that uses multiple measures of confidence to determinephysiological parameters accurately.

BRIEF SUMMARY OF THE INVENTION

The present invention includes methods, apparatuses and systems forremoving noise in physiological measurements caused by motion or othersimilar artifacts. The methods, apparatuses and systems of the presentinvention eliminate noise from light signals using a single conventionalsensor and are relatively simple computationally.

In accordance with one aspect of the invention, a method of removingmotion artifacts from electrical signals representative of attenuatedlight signals, includes transforming the electrical signals intofrequency domain data, identifying a plurality of candidate peaks fromthe frequency domain data, analyzing each of the plurality of candidatepeaks in the context of selected parameters calculated with respectthereto and arbitrating between each of the plurality of candidate peaksbased on the selected parameters to select a best frequency.

In accordance with another aspect of the invention, a method ofdetermining pulse rate and saturation from electrical signalsrepresentative of attenuated light signals and motion artifacts,includes acquiring a segment of red data and a segment of IR data fromeach of the electrical signals representative of attenuated lightsignals, transforming both the segment of red data and the segment of IRdata into red and IR frequency domain data, respectively, identifying aplurality of candidate peaks from the red and IR frequency domain data,analyzing each of the plurality of candidate peaks in the context ofselected parameters calculated with respect thereto, arbitrating betweeneach of the plurality of candidate peaks based on the selectedparameters to select a best frequency, if one exists, outputting pulserate and saturation from the best frequency, and repeating the abovesteps for new segments of data. Additionally, various quality orconfidence measures may be used to evaluate the validity of thecandidates.

A circuit card embodiment includes a processor with memory for storing acomputer program that is capable of executing instructions embodyingmethods of the invention.

A system embodiment includes an input device, an output device, a memorydevice and a motion artifact rejection circuit card capable of executinginstructions stored in the memory device implementing the methodsdescribed herein.

Finally, a system embodiment includes an input device, and outputdevice, a memory device and a processor, which may be a digital signalprocessor, capable of executing instructions stored in the memory deviceimplementing the methods described herein.

The embodiments of the present invention will be readily understood byreading the following detailed description in conjunction with theaccompanying figures of the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings illustrate various exemplary views and embodiments forcarrying out the invention. Additionally, like reference numerals in thedrawings refer to like parts in different views or embodiments.

FIG. 1 is a high-level flowchart of a method embodiment of theinvention.

FIG. 2 is two graphs showing acquired IR and red data segments.

FIG. 3 is a graph of the power spectrum of the IR data segment in FIG. 2in accordance with the invention.

FIG. 4 illustrates example graphs of measured IR and red data segmentsin accordance with the invention.

FIG. 5 is a graph of the frequency domain transformed IR signal fromFIG. 4.

FIG. 6 illustrates three graphs of IR data after filtering with threedifferent IR filters and segmented with vertical lines to show pulsesand parameter calculations according to the invention.

FIG. 7 is a block diagram of a motion artifact rejection circuit cardconfigured to remove noise artifacts from signals representing bodilyparameters in accordance with the invention.

FIG. 8 is a block diagram of a pulse oximetry system including a motionartifact rejection circuit card capable of removing noise from pulseoximetry data in accordance with the invention.

FIG. 9 is a block diagram of a pulse oximetry system including aprocessor device programmed to remove noise from pulse oximetry data inaccordance with the invention.

DETAILED DESCRIPTION OF THE INVENTION

This patent application is a continuation-in-part of U.S. patentapplication Ser. No. 09/410,991, titled METHOD, APPARATUS AND SYSTEM FORREMOVING MOTION ARTIFACTS FROM MEASUREMENTS OF BODILY PARAMETERS, filedOct. 1, 1999, pending, the contents of which are expressly incorporatedherein by reference for all purposes.

The following detailed description discloses methods, apparatuses andsystems for removing motion artifacts from measured plethysmographicwaveforms, particularly, but without limitation, those used in pulseoximetry. A system embodiment of the invention includes pulse oximetryhardware and associated software to perform the motion artifactsuppression. A method embodiment of this invention includes a series ofsteps that exploit certain characteristics of plethysmographicwaveforms. The methods, apparatuses and systems described below aresuitable for use with sensors employing light transmitted or reflectedthrough bodily tissues and substances. For convenience, the followingdetailed description will assume measurement of light that has beentransmitted through a finger of a human. The terms “signal” and“waveform” are used interchangeably herein.

FIG. 1 is a high-level flowchart of an embodiment of a method ofremoving motion artifacts from plethysmographic data and obtaining ameasure of pulse rate and SpO₂ from that data. The method steps includeacquiring segments of raw plethysmographic data 100, both a red datasegment and an IR data segment, conditioning each segment of raw datafor signal processing 110, transforming the conditioned data into thefrequency domain 120, analyzing the frequency domain data for candidatespectral peaks 130, calculating selected parameters associated with thecandidate spectral peaks 140, arbitrating between the candidate peaksbased on the selected parameters to select a best frequency 150,outputting pulse rate and median SpO₂ for the best frequency, if a bestfrequency was found 160, and repeating these steps for new raw datasegments 170, as required. The method embodiment of the invention isapplied to both red and IR data signals to eliminate or reduce noisefrom the data signals prior to outputting pulse rate and SpO₂. In thepreferred embodiment of the invention, both pulse rate and median SpO₂are output for valid best frequencies.

The method of this invention begins with acquiring a segment of data(e.g., five or more pulses or approximately ten seconds) measured from asingle light source transmitted through a finger and detected with asensor on the opposite side of the finger. Acquiring a data segment isdepicted by block 100 of FIG. 1. FIG. 2 illustrates sample segments ofIR and red data acquired according to block 100 of FIG. 1. Thehorizontal axis of FIG. 2 is measured in units of time, and specificallyhere in seconds. The vertical axis of FIG. 2 is measured in arbitraryunits, and specifically here in analog-to-digital output units. Forconvenience, a 10.24 second segment of data will be used to illustratethe method. A 10.24 second segment of data corresponds to 1024 datapoints with a sampling rate of 100 data points per second. It should bereadily apparent to one of ordinary skill in the art that the method ofthe invention is not limited to data segments of this size. The signalprocessing steps described herein may be performed on both red and IRdata segments independently and simultaneously. Thus, while the steps ofthe method may be illustrated with data from an IR light signal, thesame steps are applicable to data from a red light signal and viceversa. The terms “data segment,” “input waveform,” “data signal” and“signal” are used interchangeably herein.

A segment of data may be received from a sensor that convertstransmitted or reflected light signals into electrical signals. U.S.Pat. Nos. 5,190,038, 5,398,680, 5,448,991 and 5,820,550 to Poison etal., the disclosures of each of which are incorporated herein byreference, disclose and claim electronic systems for receiving red andIR data from a sensor, pre-conditioning the electrical signals and thenconverting the pre-conditioned electrical signals into digital datausing an analog-to-digital converter for subsequent digital signalprocessing. The raw red and IR waveforms may be sampled at anyconvenient data rate. However, for simplicity of illustration, asampling rate of 100 Hz will be assumed. Additionally, pulse rate andSpO₂ may be calculated on any convenient periodic or non-periodic basis.However, again for simplicity, we will assume that pulse rate and SpO₂are calculated on a periodic basis every ½ second.

Once a segment of data from a single electrical signal (i.e., red or IR)has been acquired and digitized, it may be conditioned for subsequentsignal processing as depicted by block 110 of FIG. 1. Signalconditioning may include filtering to reduce spectral leakage resultingfrom subsequent frequency analysis. There are several window filtersthat may be suitable for such purposes. For example, and not by way oflimitation, a Hanning window may be used to reduce spectral leakage. Itwill be readily apparent to one of ordinary skill in the art of digitalsignal processing that other window filters and methods of filteringdata to reduce spectral leakage may be selected. As such methods offiltering and various filters are known to one of ordinary skill in theart of signal processing, they will not be further detailed herein. FIG.3 illustrates the power spectrum of the IR data segment of FIG. 2 afterfiltering. The vertical axis of FIG. 3 may be measured in any arbitraryunits of power. The horizontal axis is measured in any units offrequency, specifically here in units of bpm.

The conditioned data is then transformed into the frequency domain forfurther analysis and signal processing, see block 120 of FIG. 1. Signalprocessing as described herein is generally performed in the frequencydomain. The segment of data is converted into the frequency domain by,for example, performing the conventional Fast Fourier Transform (FFT) onthe data segment. FIG. 3 is a graph of the FFT of the IR data segment ofFIG. 2. FIG. 3 illustrates a primary candidate peak at a frequency ofapproximately 70 bpm and a secondary candidate peak at a frequency ofapproximately 128 bpm. Other common techniques of converting time-domaindata to the frequency domain may also be used, e.g., classical methodsusing the FFT, such as the periodogram or correlogram, autoregressivemethods, Prony's method, minimum variance methods, maximum likelihoodmethods. Additionally, time domain data may be converted to thefrequency domain using transforms such as discrete cosine transform,wavelet transform, discrete Hartley transform, and Gabor transform. Thepreferred transform according to this method is the FFT with a windowsize of 1024 points. The 1024 data points are placed in a buffer, theFFT buffer. The FFT transforms the 1024 points of data from the timedomain into the frequency domain. The output of the FFT is 512 points ofreal and 512 points of imaginary data in the frequency domain. Fromthese 512 points of real and 512 points of imaginary data the powerspectrum is calculated and placed in a power spectrum buffer.

Both transient and periodic noise artifacts can induce peaks in thefrequency domain that may be larger than the peak associated with thepatient's heart rate. The frequency peak that actually represents thepatient's heart rate (best frequency) must then be determined. Analyzingthe power spectrum peaks to determine candidate spectral peaks isdepicted in block 130 of FIG. 1. One approach to determining the bestfrequency would be to order the frequencies by peak amplitude fromlargest to smallest, F₁ to F_(n), where F₁ through F_(n) are notharmonics of each other, and analyze them one by one to find the correctfrequency, i.e., the patient's heart rate. However, a preferred methodselects up to three candidate spectral peaks for further analysis.

The function of block 130 is to locate candidate spectral peaks from thepower spectrum computed in block 120. The power spectrum buffer is anarray of 512 vector points (referred to herein as “bins”) in thefrequency domain. Each array element in the power spectrum bufferrepresents the power of the corresponding frequency in the original rawdata waveform. Of the 512 bins, only bins 5 (29 bpm) through 43 (252bpm) are of interest, since this range covers the physiological limitsof the human heart rate. All other bins are unused by the method of theinvention because they cannot physiologically represent a valid spectralfrequency of a pulse rate. Table 1, below, shows the first 45 points ofthe power spectrum array.

TABLE 1 Power Spectrum Buffer Frequency (Hz) Pulse Rate (bpm) bin numbern f = n* 100/1024 Pulse Rate = f* 60 0 0.00000 0.0 1 0.09766 5.9 20.19531 11.7 3 0.29297 17.6 4 0.39063 23.4 5 0.48828 29.3 6 0.58594 35.27 0.68359 41.0 8 0.78125 46.9 9 0.87891 52.7 10 0.97656 58.6 11 1.0742264.5 12 1.17188 70.3 13 1.26953 76.2 14 1.36719 82.0 15 1.46484 87.9 161.56250 93.8 17 1.66016 99.6 18 1.75781 105.5 19 1.85547 111.3 201.95313 117.2 21 2.05078 123.0 22 2.14844 128.9 23 2.24609 134.8 242.34375 140.6 25 2.44141 146.5 26 2.53906 152.3 27 2.63672 158.2 282.73438 164.1 29 2.83203 169.9 30 2.92969 175.8 31 3.02734 181.6 323.12500 187.5 33 3.22266 193.4 34 3.32031 199.2 35 3.41797 205.1 363.51563 210.9 37 3.61328 216.8 38 3.71094 222.7 39 3.80859 228.5 403.90625 234.4 41 4.00391 240.2 42 4.10156 246.1 43 4.19922 252.0 444.29688 257.8

In table 1, column 1 is the bin number, n; column 2 is the centerfrequency, f, of the corresponding bin number, n, calculated as theproduct of the bin number and sampling rate (100 samples/sec) divided bythe block size used by the FFT (i.e., 1024); and column 3 is the pulserate corresponding to the center frequency, f, of column 2, calculatedby multiplying f (measured in units of beats per second) by 60 toconvert to units of beats per minute.

In order to select candidate peaks (and corresponding frequencies),different amplitude analysis methods are applied to different frequencybands. The amplitude of adjacent and nearby frequency components of thecandidate peak amplitude may be compared in terms of their absolute orrelative values. For example, the frequencies represented by candidatebins 5 through 10 (“5-10” search method) may be stepped through in asequential fashion. According to the “5-10” search method, a candidatebin is assumed to be a candidate power spectrum peak if the previousthree bins and subsequent four bins relative to a candidate bin are alllower in power than the candidate bin. For example, in order for bin 6to be a candidate spectral peak, bins 3, 4, 5, 7, 8, 9 and 10 must allbe lower in power than bin 6. The terms “spectral peak,” “power peak,”or simply “peak” are used synonymously herein. Various amplitude, shape,syntactic or other pattern analysis methods may be applied to identify acandidate peak. Also, multiple curve fit methods, as known to one ofordinary skill in the spectroscopic analysis, may also be applied.

Once all possible power spectrum peak candidates are found,predetermined criteria are applied to select, at most, three candidatespectral peaks. First, the power peak associated with the largest poweramplitude is selected to be the primary candidate peak. Then, any powerpeaks that are determined to be harmonics of the primary candidate powerpeak are eliminated. According to the method, a harmonic is defined asany power peak the frequency of which is a multiple of the primary peak,±1 bin, and the amplitude of which is less than half the maximum allowedpower of the previous harmonic, or in the case of the first harmonic,less than half the power of the primary peak. For example, assume aprimary candidate peak is found at bin 10. Possible harmonic bins of 10are bins 19-21, 29-31 and 39-41. Continuing with the example, if theprimary power peak amplitude (bin 10) is 100 arbitrary power spectrumunits, then bins 19-21 must be less than 50 units to be deemed aharmonic, bins 29-31 must be less than 25 units and bins 39-41 must beless than about 12 units, where units are the measure of the amplitudeof the power spectrum. Other weights may be applied to the analysis ofthe sequence for detection of harmonics of the candidate spectral peakwithout departing from the scope of the invention.

After harmonics of the primary candidate peak are eliminated, the nextlargest remaining power peak found (if any) is selected to be thesecondary candidate peak. Finally, if the previous pulse rate isnon-zero, the power spectrum corresponding to the previous pulse rate isdetermined. If the bin corresponding to the previous pulse rate is notequal to the primary or secondary candidate power peak, then the bincorresponding to the previous pulse rate is selected to be the tertiarycandidate peak. Thus, up to three candidate peaks (primary, secondaryand tertiary) and corresponding frequencies of each candidate peak areidentified in block 130 of FIG. 1.

Block 140 of FIG. 1 depicts calculating selected parameters associatedwith the candidate peaks identified in block 130. Block 140 may includepulse window filtering, and calculating such parameters as peakdetection, pulse rejection criteria and descriptive parametersassociated with each of the up to three candidate power peaks found fromblock 130. These parameters are used to determine a pulse confidence foreach candidate peak. The parameters calculated according to theinvention for each filtered candidate peak include measures of centraltendency and variability of pulse width, pulse rate and SpO₂, as well asmeasures of the history and confidence of these parameters. A preferredembodiment of the present invention includes parameters such as: (1)Window Pulse Rate, (2) Pulse Width Variability, (3) SpO₂ Variability,(4) Pulse Window SpO₂, (5) Pulse Peak Amplitude Variability, (6) PulseRate History Percentage, and (7) Pulse Window Confidence. It should benoted that other parameters including, but not limited to, othermeasures of central tendency, variability (i.e., skewness, kurtosis),history/trend and confidence, could be calculated from the candidatepower peaks without departing from the scope of the invention. Each ofthe parameters listed is discussed in greater detail below, beginningwith pulse window filtering.

Prior to calculating the aforementioned parameters, each candidate peakmay be filtered with a narrow band filter, such as a narrow band pass,finite impulse response (FIR) filter. In one aspect of the presentinvention, one of several predefined FIR filters is applied to a givenbin or candidate peak. The peak frequencies of the filters may beseparated by a fixed difference in frequency (measured in Hz or bpm),such as 25 bpm, or may be variable and a function of either frequency ora characteristic of the spectrum, for example variability or noise, orboth. For example, if a candidate peak was found at bin 12, a filterwith center or peak frequency of 76.2 bpm might be chosen. A fixeddifference in frequency may be in a range from about 15 bpm to about 40bpm. Likewise, a variable difference in frequency may be in a range fromabout 15 bpm to about 40 bpm.

Preferably, to improve discrimination, especially with closely spacedpeaks, the band pass filter coefficients may be stored or generated andadjusted as needed so that the center frequency is nearly identical tothe candidate frequency. Additionally, other filtering methods such as(a) other types of band pass filters, i.e., infinite impulse response(IIR) filters, and (b) frequency domain methods such as transforming thedata into the frequency domain (for example, FFT), filtering oreliminating unwanted components in the frequency domain and transformingback into the time domain (for example, inverse FFT) for further signalprocessing, may be applied.

Once the up to three peak candidates are selected and filtered, a peakdetector algorithm is applied to each of the up to three candidate peaksin the time domain. The function of the peak detector algorithm is toidentify power spectrum peaks in each of the filtered time domain datasegments and their associated center frequencies. The terms “window” and“pulse window” are used interchangeably with “time domain data segment”herein. For each peak found in the time domain, the pulse width iscalculated as the time between each peak. The Window Pulse Rate iscalculated by dividing the sum of the pulse width time of all peaks bythe number of peaks detected.

Pulse Width Variability, a measure of how consistent the pulse width isfor all the peaks in a given pulse window, is calculated according tothe method of the invention. With the exception of subjects presentingcardiac arrhythmias, particularly ventricular arrhythmias, thevariability of the pulse width of all the peaks should be low within ashort time interval such as a 10.24 second window. Higher pulse widthvariability often is an indication of either (a) cardiac arrhythmias or(b) physiological artifacts such as motion. Pulse Width Variability iscalculated as the sum of absolute differences between individual pulsewidths and the average pulse width normalized by the average pulsewidth: $\begin{matrix}{{{PulseWidthVariability} = \frac{\sum\limits_{i}\left| {{AveragePulseWidth} - {PulseWidth}_{i}} \right|}{AveragePulseWidth}},} & (3)\end{matrix}$

where, i is the number of peaks detected in the window, Pulse Width, isthe pulse width for the ith peak, and Average Pulse Width is the sum ofthe individual pulse widths divided by the number of pulses. Forexample, a pulse rate of 180 bpm has a pulse width of 330 ms; and apulse rate of 60 bpm has a pulse width of 1000 ms. If an average pulsewidth difference was found to be 100 ms, this would have a much greatereffect at 180 bpm than 60 bpm. Thus, dividing by the pulse ratenormalizes the pulse width variability. Returning to the 180 bpm versus60 bpm example, dividing by the Average Pulse Width causes the PulseWidth Variability to be 3 times greater at 180 bpm than at 60 bpm.

SpO₂ is calculated for each peak in the pulse window using the ratio, R,which is “mapped” to SpO₂ via a lookup table. This ratio, R, is definedas: $\begin{matrix}{{R = \frac{\left( \frac{R\quad e\quad d\quad A\quad C\quad C\quad o\quad m\quad p\quad o\quad n\quad e\quad {nt}}{R\quad e\quad d\quad D\quad C\quad C\quad o\quad m\quad p\quad o\quad n\quad e\quad {nt}} \right)}{\left( \frac{I\quad R\quad A\quad C\quad C\quad o\quad m\quad p\quad o\quad n\quad e\quad {nt}}{{IR}\quad D\quad C\quad C\quad o\quad m\quad p\quad o\quad n\quad e\quad {nt}} \right)}},} & (4)\end{matrix}$

The ratio, R, in equation 4 is used to index into an empirically derivedtable to determine SpO₂. The IR AC Component is chosen at the point ofmaximum negative slope between the peak and valley for each peak of thefiltered IR waveform. The red AC Component is the slope of the filteredred waveform at the time coincident with the above selected IR ACComponent. The peak and valley points from the IR filtered waveforms aretransposed onto the raw red and IR waveform. The average between thepeak and the valley is considered the DC component (analogous to a DCoffset for a positively biased AC waveform). This DC component iscalculated for both the red and IR waveforms (i.e., red DC Component andIR DC Component). This process is repeated for each of the up to threeband pass filtered pulse windows corresponding to the candidate peaksidentified in block 130 of FIG. 1. The term “pulse window” is usedherein to represent time domain data corresponding to a particularcandidate peak that has been band pass filtered.

SpO₂ Variability, a measure of how consistent the SpO₂ is for all thepeaks in a pulse window, is calculated according to the method of theinvention. Under typical conditions, SpO₂ Variability is low, oftenwithin ±2 percent saturation over a short time interval such as the10.24 second pulse window. When the pulse window is filtered by afrequency that is not related to the pulse rate (e.g, random noise), theSpO₂ Variability tends to be high. Therefore, SpO₂ Variability is a goodmeasure for determining confidence in a pulse window. SpO₂ Variabilityis calculated as the sum of the absolute difference between theindividual SpO₂ values and the average SpO₂ for the pulse window.$\begin{matrix}{{{{SpO}_{2}\quad {Variability}} = {\sum\limits_{i}\left| {{AverageSpO}_{2} - {SpO}_{2,i}} \right|}},} & (5)\end{matrix}$

where i is the number of peaks detected in the window, and SpO_(2,i) isthe saturation calculated for the ith peak detected and Average SpO₂ issum of the individual SpO₂ values divided by the number of individualSpO₂values.

The Pulse Window SpO₂ is calculated by the method of the invention asthe median value of all of the SpO₂ values within the current pulsewindow. Other methods of determining central tendency may be usedincluding, but not limited to, a weighted mean or average.

The Pulse Peak Amplitude Variability, a measure of the consistency ofthe amplitude of the pulse peaks in a pulse window, is calculatedaccording to the method of the invention. Pulse Peak AmplitudeVariability is calculated as the sum of the differences between theindividual pulse peak amplitudes and the average pulse peak amplitudefor the pulse window.

Motion artifacts are usually not purely rhythmic in nature. Therefore,the portion of the power spectrum comprising motion artifacts changesdynamically as the spectrum of the motion artifacts changes. Incontrast, the spectrum of the underlying pulse rate varies much lessover longer periods of time relative to motion artifact spectrum.

Pulse Rate History Percentage, another parameter useful for detectingmotion artifacts, is calculated by the method of the invention.According to the method, a pulse rate is calculated for the primary andsecondary candidate peaks and these pulse rate calculations are saved inmemory. This memory may be any capacity but preferably is capable ofstoring at least between 10-60 seconds of pulse rates (for the primaryand secondary candidate peaks) and is updated periodically with thenewest values overwriting the oldest values. For illustration purposes,assume the memory stores 30 seconds of pulse rates for the primary andsecondary peaks. Pulse Rate History Percentage is calculated accordingto the method of the invention as the percentage that the pulse ratecorresponding to the candidate peak occurred in a given period of time,e.g., the most recent 30 seconds. Of course, one of ordinary skill inthe art may recognize that a Pulse Rate History Percentage may becalculated in other analogous ways. For example, there could be a longerhistory of pulse rates (i.e., more or less than 30 seconds) and it couldbe weighted or filtered in various manners without departing from thescope of the invention.

A pulse window under evaluation may be rejected from further processingand flagged as an invalid pulse window if certain criteria are met. Apulse window under evaluation may be checked against the followingcriterion:

1. The number of peaks in the pulse window, i, is two or less (i≦2)

2. The Window Pulse Rate is zero (i.e., no frequency found).

Additional criteria related to variability and history may include thefollowing:

1. The Pulse Rate History Percentage is less than a percentage of asignificant portion of the pulse. According to the preferred embodimentof the invention, a percentage of a significant portion of the pulsewould be in the range from about 25% to about 30%.

2. The SpO₂ Variability is greater than “normal” variation in SpO₂ ineither absolute or relative terms. According to the preferred embodimentof the invention, SpO₂ Variability greater than a threshold ranging fromabout 3% to about 5% is greater than “normal” variation in SpO₂.

3. The Pulse Width Variability is greater than a threshold representingexcessive variations. According to the preferred embodiment of theinvention, a threshold representing excessive variations may fall withinthe range of about 200 to about 400 points for a pulse window of 1024points.

4. The Window Pulse Rate differs by more than an excessive amount eitherin absolute or relative terms from the center frequency of the candidatepower spectrum peak. According to the preferred embodiment of theinvention, an excessive amount is a threshold greater than about 20 bpmto about 30 bpm, or about 20% to about 35% of the center frequency,whichever maximum threshold is smaller.

If the pulse window under evaluation meets any of these criteria, thenthe pulse window under evaluation is rejected as invalid and flagged assuch. Also, the optimal thresholds and values for each of the abovecriteria may be optionally adjusted by methods known to one of ordinaryskill in the art, including but not limited to, learning or searchmethods.

A confidence measure, Pulse Window Confidence, is also calculatedaccording to block 140 of FIG. 1. According to the preferred method ofthe invention, Pulse Window Confidence is calculated as a weighted sumof the Pulse Width Variability, SpO₂ Variability, Pulse AmplitudeVariability and the Pulse Rate History Percentage parameters. The lowerthe value of Pulse Window Confidence measure, the higher the confidencethat the candidate peak under evaluation is a valid pulse rate. ThePulse Window Confidence, which is a point value without units, is onlycomputed for each of the up to three remaining candidate peaks and thenpassed to the arbitrating step, see block 150 of FIG. 1.

The function of the arbitrating step, block 150 of FIG. 1, is todetermine which, if any, of the up to three candidate peaks should beaccepted. The arbitrating step 150, is accomplished by evaluating thecalculated parameters, including the confidence or quality measures,(i.e., Pulse Window Confidence), of the candidate peaks relative to oneanother. Some of the candidate peaks may already have been flagged as aninvalid pulse window, and thus, are not evaluated further. If none ofthe up to three candidate peaks is valid, no new pulse rate or newsaturation will be displayed according to the method of the invention.Alternatively, if none of the up to three candidate peaks are valid,another algorithm (other than the method of the invention) may beemployed to determine the pulse rate and saturation, see for exampleU.S. Pat. Nos. 5,190,038, 5,398,680, 5,448,991 and 5,820,550 to Polsonet al. Arbitration is then conducted among the up to three remainingcandidate peaks in order to determine which, if any candidate peak,should be selected as the best frequency. The arbitration is preferablyexecuted in the sequence presented below.

1. If the primary candidate peak frequency, f₁, is zero, then there isno valid candidate peak (i.e., no best frequency).

2. If the tertiary candidate peak Pulse Window Confidence is less thanthe Pulse Window Confidence for either the primary candidate peak or thesecondary candidate peak, then the tertiary candidate peak is the bestfrequency. Recall that the lower the Pulse Window Confidence value, thehigher the confidence that the candidate peak is the true pulse rate.

3. If the primary candidate peak and the secondary candidate peak haveboth been rejected, then there is no valid candidate peak (i.e., no bestfrequency).

4. If the primary candidate peak has not been rejected and the secondarycandidate peak has been rejected, then the primary candidate peak is thebest frequency.

5. If the primary candidate peak has been rejected and the secondarycandidate peak has not been rejected, then the secondary candidate peakis the best frequency.

6. If the primary candidate peak Pulse Window Confidence is greater thanthe secondary candidate peak Pulse Window Confidence by a specifiedthreshold, t₁, and the primary candidate peak Pulse Rate HistoryPercentage is greater than another specified threshold, t₂, then theprimary candidate peak is the best frequency. Similar criteria apply ifthe secondary candidate peak Pulse Window Confidence is greater than theprimary candidate peak.

7. If the secondary candidate peak frequency, f₂, is a rough harmonic ofthe primary candidate peak frequency, f₁, and the Pulse WindowConfidence of the primary candidate peak is not more than a specifiednumber of points greater than the Pulse Window Confidence of thesecondary candidate peak, then accept the primary candidate peak.Secondary candidate peak frequency, f₂, is a rough harmonic of theprimary candidate peak frequency, f₁, if the candidate frequency iswithin a frequency tolerance of approximately ±10 bpm. Again, similarcriteria apply if the secondary candidate peak is a rough harmonic ofthe primary candidate peak.

8. If the Pulse Window Confidence of the primary candidate peak is nomore than a specified number of points greater than the Pulse WindowConfidence of the secondary candidate peak, then accept the primarycandidate peak. Otherwise, accept the secondary candidate peak.

Once a candidate peak has been accepted (as the best frequency)according to the arbitrating step 150, the pulse rate and SpO₂ arecalculated for the best frequency and output, for example, to a displayor monitor, as depicted in block 160 of FIG. 1. The steps 100-160 maythen be repeated for any new segments of data as depicted in decisionblock 170 of FIG. 1. The above sequence is exemplary only, and notintended to be limiting. Furthermore, one of ordinary skill in the artwill recognize that the various criteria selected to evaluate pulseshape may be assigned weights to emphasize relative importance.

FIGS. 4-6 illustrate exemplary graphical results from application of thepreferred method of the invention. FIG. 4 shows graphs of measured IRand red data segments in accordance with the invention. FIG. 5 is agraph of the frequency domain transformed IR signal from FIG. 4, showinga primary candidate peak at approximately 82 bpm, a secondary candidatepeak at approximately 105 bpm.

FIG. 6 illustrates three graphs of IR data after filtering with threedifferent FIR filters and segmented with vertical lines to delineatepulses and parameter calculations according to the invention. Theparameter calculations displayed to the right in the graphs shown inFIG. 6 are exemplary only, and are not necessary for practicing theinvention. With respect to those parameter calculations displayed,“PRHist” corresponds to Pulse Rate History Percentage; “PR” correspondsto Window Pulse Rate; “PWVar” corresponds to Pulse Width Variability;“MSat” corresponds to Pulse Window SpO2; “SVar” corresponds to SpO₂Variability; “PConf” corresponds to Pulse Window Confidence; “Conf OK”corresponds to an accepted candidate peak or the best frequency, and“R:xxxx” corresponds to a notation that the candidate peak underevaluation has been rejected for the reason “xxxx,” i.e., a parametercalculation has concluded with a rejection of the candidate peak all asdisclosed herein.

The top graph in FIG. 6 represents the pulse window corresponding to theprimary candidate peak at approximately 82 bpm as shown in FIG. 5. Themiddle graph in FIG. 6 represents the pulse window corresponding to thesecondary candidate peak at approximately 105 bpm as shown in FIG. 5.The bottom graph in FIG. 6 represents the pulse window corresponding tothe tertiary candidate peak at approximately 158 bpm as shown in FIG. 5.Note that in this instance, the tertiary candidate at frequency of 158bpm has the lowest Pulse Window Confidence and no rejections based oncalculated parameters. Note that FIGS. 4-6 are merely exemplary graphsillustrating sample calculations based on actual data obtained fromtypical pulse oximetry measurements

The methods described above may be integrated into apparatuses and/orsystems for calculating blood oxygen saturation. Referring to FIG. 7,one apparatus embodiment of this invention comprises a motion artifactrejection circuit card 10 with an I/O device 11, a processor 12 andmemory 14 for storing a computer programmed algorithm for motionartifact rejection as described in the above methods. Processor 12 maybe a digital signal processor. I/O device 11 may be any circuitry thatallows communication to and from external circuitry, for example, andnot by way of limitation, bus interface circuitry. I/O device 11 mayinclude a circuit card edge connector for plugging into a pulse oximetrymonitor system. Memory 14 may be any solid-state electronic memorysuitable for storing digital data including, for example, computer codeand measurement data.

Referring to FIG. 8, the motion artifact rejection circuit card 10 ofFIG. 7 may be incorporated in a complete pulse oximetry system 16 foreliminating motion-induced noise artifacts in electrical signals (asdescribed in the method embodiments above) and calculating anddisplaying physiological parameters, either as a discrete circuit cardor as part of a larger circuit card, such as a motherboard, controllingother functions of the pulse oximetry system 16. The pulse oximetrysystem 16 also includes an input device 18 and an output device 20.Input device 18 may be a pulse oximeter sensor with red and IR LED lightsources and a photodetector to convert transmitted or reflected lightinto an electrical signal. Output device 20 may be a display device suchas a cathode ray tube device, liquid crystal display, active matrixdisplay or any other suitable display device known to one of skill inthe art. Alternatively, output device 20 may be a printer for producinga permanent or written record such as a laser printer, ink jet printer,thermal printer, dot matrix printer or any other suitable printer knownto one of skill in the art. The pulse oximetry system 16 may be anypulse oximeter that uses the principles of operation as described above.A particular pulse oximeter for which the circuit card embodiment asdescribed above is suitable for use is the Respironics, Inc. (formerlyNovametrix Medical Systems, Inc.), Model 520A, Pulse Oximeter.

Referring to FIG. 9, a block diagram of a pulse oximetry system 22including a processor device 12, an input device 18, an output device 20and a storage device 24, is shown. Input device 18 may be a pulseoximeter sensor with red and IR LED light sources and a photodetector toconvert transmitted or reflected light into an electrical signal. Outputdevice 20 may be a display device such as a cathode ray tube device,liquid crystal display, active matrix display or any other suitabledisplay device known to one of skill in the art. Alternatively, outputdevice 20 may be a printer for producing a permanent or written recordsuch as a laser printer, ink jet printer, thermal printer, dot matrixprinter or any other suitable printer known to one of skill in the art.Storage device 24 may be a disk drive, or any kind of solid-stateelectronic memory device suitable for storing digital data including,for example, computer code and measurement data.

Although this invention has been described with reference to particularembodiments, the invention is not limited to these describedembodiments. Rather, it should be understood that the embodimentsdescribed herein are merely exemplary and that a person skilled in theart may make many variations and modifications without departing fromthe spirit and scope of the invention. All such variations andmodifications are intended to be included within the scope of theinvention as defined in the appended claims.

What is claimed is:
 1. A method of removing motion artifacts fromelectrical signals representative of attenuated light signals,comprising: transforming the electrical signals into frequency domaindata; identifying a plurality of candidate peaks from the frequencydomain data, wherein the identifying includes eliminating harmonicfrequencies from the plurality of candidate peaks such that no two ofthe plurality of candidate peaks comprise harmonics of one another;narrow band pass filtering at each of the plurality of candidate peaks;developing parameters associated with each of the plurality of candidatepeaks; analyzing each of the plurality of candidate peaks with respectto at least some of the developed parameters; and arbitrating between atleast some of the plurality of candidate peaks employing at least someof the developed parameters to select a best frequency.
 2. The method ofclaim 1, further comprising conditioning the electrical signals toreduce spectral leakage prior to the transforming step.
 3. The method ofclaim 2, wherein the conditioning includes filtering the electricalsignals.
 4. The method of claim 3, wherein the filtering is performedwith a Hanning window.
 5. The method of claim 1, wherein thetransforming the electrical signals into frequency domain data isperformed with a fast Fourier transform.
 6. The method of claim 1,wherein the transforming the electrical signals into frequency domaindata is performed with a technique selected from the group consisting ofa periodogram, a correlogram, autoregressive methods, Prony's method,minimum variance methods, maximum likelihood methods, a discrete cosinetransform, a wavelet transform, a discrete Hartley transform and a Gabortransform.
 7. The method of claim 1, wherein the identifying theplurality of candidate peaks further comprises: assigning a largestpower amplitude from the frequency domain data as a primary candidatepeak; assigning a next largest power amplitude as a secondary candidatepeak; and assigning a previous non-zero pulse rate as a tertiarycandidate peak if the previous non-zero pulse rate is neither theprimary candidate peak nor the secondary candidate peak.
 8. The methodof claim 1, wherein the identifying the plurality of candidate peaksfurther comprises identifying n peaks, by frequency, F₁ to F_(n), indescending order of peak amplitude.
 9. The method of claim 1, whereinthe narrow band pass filtering comprises finite impulse responsefiltering.
 10. The method of claim 1, wherein the narrow band passfiltering comprises infinite impulse response filtering.
 11. The methodof claim 1, wherein the narrow band pass filtering comprises filteringeach of the plurality of candidate peaks with one of n narrow bandfilters to mask influence of candidate frequencies not under evaluation.12. The method of claim 11, wherein n=8 and wherein each of the eightnarrow band filters is separated by a fixed difference in centerfrequency in a range of approximately 25 bpm to approximately 30 bpm.13. The method of claim 11, wherein each of the n narrow band filters isseparated by a variable difference in center frequency in a range ofapproximately 25 bpm to approximately 30 bpm.
 14. The method of claim 1,wherein the narrow band pass filtering comprises filtering each of theplurality of candidate peaks with a narrow band filter of variablecenter frequency.
 15. The method of claim 1, wherein the narrow bandpass filtering comprises filtering each of the plurality of candidatepeaks with a narrow band filter wherein filter coefficients aregenerated and adjusted so that a center frequency of the narrow bandfilter is approximately a center frequency associated with each of thecandidate peaks.
 16. The method of claim 1, wherein the narrow band passfiltering comprises filtering each of the plurality of candidate peaksusing a fast Fourier transform (FFT), narrow band filter and inverseFFT.
 17. The method of claim 1, wherein the analyzing each of theplurality of candidate peaks with respect to at least some of thedeveloped parameters includes calculating a window pulse rate.
 18. Themethod of claim 1, wherein the analyzing each of the plurality ofcandidate peaks with respect to at least some of the developedparameters includes calculating pulse width variability.
 19. The methodof claim 1, wherein the analyzing each of the plurality of candidatepeaks with respect to at least some of the developed parameters includescalculating SpO₂ variability.
 20. The method of claim 1, wherein theanalyzing each of the plurality of candidate peaks with respect to atleast some of the developed parameters includes calculating pulse windowSpO₂.
 21. The method of claim 1, wherein the analyzing each of theplurality of candidate peaks with respect to at least some of thedeveloped parameters includes calculating pulse rate history percentage.22. The method of claim 1, wherein the analyzing each of the pluralityof candidate peaks with respect to at least some of the developedparameters includes calculating pulse window confidence.
 23. The methodof claim 22, wherein the calculating pulse window confidence includescalculating a weighted sum of pulse width variability, SpO₂ variabilityand pulse rate history percentage.
 24. The method of claim 1, whereinthe analyzing each of the plurality of candidate peaks with respect toat least some of the developed parameters includes calculating a windowpulse rate, pulse width variability, SpO₂ variability, pulse windowSpO₂, pulse rate history percentage and pulse window confidence.
 25. Themethod of claim 1, wherein the arbitrating between at least some of theplurality of candidate peaks employing at least some of the developedparameters includes applying a predetermined criteria to select the bestfrequency.
 26. The method of claim 1, wherein the plurality of candidatepeaks comprises up to three candidate peaks, including a primarycandidate peak, a secondary candidate peak and a tertiary candidatepeak.
 27. The method of claim 26, wherein the arbitrating between atleast some of the candidate peaks includes applying the followingcriteria using at least some of the developed parameters to select thebest frequency: if a primary candidate peak frequency is zero, thenthere is no valid candidate peak; if a tertiary candidate peak pulsewindow confidence is less than a pulse window confidence for either theprimary candidate peak or the secondary candidate peak, then thetertiary candidate peak is the best frequency; if the primary candidatepeak and the secondary candidate peak have both been rejected, thenthere is no valid candidate peak; if the primary candidate peak has notbeen rejected and the secondary candidate peak has been rejected, thenthe primary candidate peak is the best frequency; if the primarycandidate peak has been rejected and the secondary candidate peak hasnot been rejected, then the secondary candidate peak is the bestfrequency; if the primary candidate peak pulse window confidence isgreater than the secondary candidate peak pulse window confidence by afirst threshold, t1, and the primary candidate peak pulse rate historypercentage is greater than a second threshold, t2, then the primarycandidate peak is the best frequency; if the secondary candidate peakfrequency is a rough harmonic of the primary candidate peak frequencyand the pulse window confidence of the primary candidate peak is notmore than a specified number of points greater than the pulse windowconfidence of the secondary candidate peak, then accept the primarycandidate peak; and if the pulse window confidence of the primarycandidate peak is no more than a specified number of points greater thanthe pulse window confidence of the secondary candidate peak, then theprimary candidate peak is the best frequency, otherwise, the secondarycandidate peak is the best frequency.
 28. The method of claim 27,wherein the arbitration is conducted in the sequence presented in claim27.
 29. A method of determining pulse rate and blood oxygen saturationfrom electrical signals representative of attenuated light signals andmotion artifacts, comprising: acquiring a segment of red data and asegment of IR data from each of the electrical signals representative ofattenuated light signals; transforming both the segment of red data andthe segment of IR data into red and IR frequency domain data,respectively; identifying a plurality of candidate peaks from the redand IR frequency domain data, wherein the identifying includeseliminating harmonic frequencies from the plurality of candidate peakssuch that no two of the plurality of candidate peaks comprise harmonicsof one another; narrow band pass filtering at each of the plurality ofcandidate peaks; developing parameters associated with each of theplurality of candidate peaks; analyzing each of the plurality ofcandidate peaks with respect to at least some of the developedparameters; arbitrating between at least some of the plurality ofcandidate peaks employing at least some of the developed parameters toselect a best frequency; outputting pulse rate and blood oxygensaturation relating to the best frequency; and repeating the above stepsfor new segments of data.
 30. The method of claim 29, wherein thetransforming includes performing a fast Fourier transform.
 31. Themethod of claim 29, wherein the identifying the plurality of candidatepeaks comprises: assigning a largest power amplitude from the red and IRfrequency domain data as a primary candidate peak; assigning a nextlargest power amplitude as a secondary candidate peak; assigning aprevious non-zero pulse rate as a tertiary candidate peak if theprevious non-zero pulse rate is neither the primary candidate peak northe secondary candidate peak.
 32. The method of claim 29, wherein thedeveloped parameters include pulse width variability calculated as a sumof absolute differences between individual pulse widths and an averagepulse width normalized by the average pulse width.
 33. The method ofclaim 29, wherein the developed parameters include SpO₂ variabilitycalculated as a sum of absolute difference between individual SpO₂values and an average SpO₂ for a given pulse window.
 34. The method ofclaim 29, wherein the developed parameters include pulse window SpO₂calculated by taking a measure of central tendency of all individualSpO₂ calculations in a given pulse window.
 35. The method of claim 29,wherein the developed parameters include pulse peak amplitudevariability calculated as a sum of differences between individual pulsepeak amplitudes and average pulse peak amplitude for a given pulsewindow.
 36. The method of claim 29, wherein the developed parametersinclude pulse rate history percentage calculated as a percentage of timethat a pulse rate corresponding to a candidate peak has occurred in agiven period of time.
 37. The method of claim 29, wherein the developedparameters include pulse window confidence calculated as a weighted sumof pulse width variability, SpO₂ variability, pulse amplitudevariability and pulse rate history percentage.
 38. The method of claim29, wherein the arbitrating between at least some of the plurality ofcandidate peaks employing at least some of the developed parametersincludes applying selection criteria to select the best frequency.
 39. Amethod of determining pulse rate and blood oxygen saturation fromelectrical signals representative of attenuated light signals and motionartifacts, comprising: acquiring a segment of red data and a segment ofIR data from each of the electrical signals representative of attenuatedlight signals; transforming both the segment of red data and the segmentof IR data into red and IR frequency domain data, respectively;identifying a plurality of candidate peaks from the red and IR frequencydomain data; narrow band pass filtering at each of the plurality ofcandidate peaks; developing parameters associated with each of theplurality of candidate peaks wherein the developed parameters includewindow pulse rate calculated by dividing a sum of all pulse width timesof all peaks in a data segment by quantity of peaks detected in the datasegment; analyzing each of the plurality of candidate peaks with respectto at least some of the developed parameters; arbitrating between atleast some of the plurality of candidate peaks employing at least someof the developed parameters to select a best frequency; outputting pulserate and blood oxygen saturation relating to the best frequency; andrepeating the above steps for new segments of data.
 40. The method ofclaim 39, wherein the transforming includes performing a fast Fouriertransform.
 41. The method of claim 39, wherein the identifying theplurality of candidate peaks comprises: assigning a largest poweramplitude from the red and IR frequency domain data as a primarycandidate peak; assigning a next largest power amplitude as a secondarycandidate peak; and assigning a previous non-zero pulse rate as atertiary candidate peak if the previous non-zero pulse rate is neitherthe primary candidate peak nor the secondary candidate peak.
 42. Themethod of claim 39, wherein the developed parameters include pulse widthvariability calculated as a sum of absolute differences betweenindividual pulse widths and an average pulse width normalized by theaverage pulse width.
 43. The method of claim 39, wherein the developedparameters include SpO₂ variability calculated as a sum of absolutedifference between individual SpO₂ values and an average SpO₂ for agiven pulse window.
 44. The method of claim 39, wherein the developedparameters include pulse window SpO₂ calculated by taking a measure ofcentral tendency of all individual SpO₂ calculations in a given pulsewindow.
 45. The method of claim 39, wherein the developed parametersinclude pulse peak amplitude variability calculated as a sum ofdifferences between individual pulse peak amplitudes and average pulsepeak amplitude for a given pulse window.
 46. The method of claim 39,wherein the developed parameters include pulse rate history percentagecalculated as a percentage of time that a pulse rate corresponding to acandidate peak has occurred in a given period of time.
 47. The method ofclaim 39, wherein the developed parameters include pulse windowconfidence calculated as a weighted sum of pulse width variability, SpO₂variability, pulse amplitude variability and pulse rate historypercentage.
 48. The method of claim 39, wherein the arbitrating betweenat least some of the plurality of candidate peaks employing at leastsome of the developed parameters includes applying selection criteria toselect the best frequency.
 49. A circuit card for use in a pulseoximetry system to remove motion-induced noise artifacts from attenuatedlight signals, the circuit card comprising: a circuit board for mountingelectronic circuitry and interfacing with the pulse oximetry system; aprocessor mounted on the circuit board for processing input signalsaccording to instructions; and a memory storing a computer program,wherein the memory is operably coupled to the processor, and wherein thecomputer program includes instructions for implementing a method ofremoving motion artifacts from the attenuated light signals, the methodcomprising: acquiring a segment of red data and a segment of IR datafrom the attenuated light signals to obtain electrical signalsrepresentative of the attenuated light signals; conditioning theelectrical signals to reduce spectral leakage; transforming theelectrical signals into frequency domain data; identifying a pluralityof candidate peaks from the frequency domain data, wherein theidentifying includes eliminating harmonic frequencies from the pluralityof candidate peaks such that no two of the plurality of candidate peakscomprise harmonics of one another; narrow band pass filtering at each ofthe plurality of candidate peaks; developing parameters associated witheach of the plurality of candidate peaks; analyzing each of theplurality of candidate peaks with respect to at least some of thedeveloped parameters; arbitrating between at least some of the pluralityof candidate peaks employing at least some of the developed parametersto select a best frequency; and repeating the above steps with a newsegment of data.
 50. The circuit card of claim 49, wherein the processoris a digital signal processor.
 51. The circuit card of claim 49, furtherconfigured for calculating pulsatile blood oxygen concentration, SpO₂,using the best frequency.
 52. The circuit card of claim 49, furtherconfigured for calculating pulse rate using the best frequency.
 53. Apulse oximeter for removing motion-induced noise artifacts fromelectrical signals representative of attenuated light signals comprisingan input device, an output device, and a motion artifact circuit card,wherein the motion artifact circuit card comprises: a circuit board formounting electronic circuitry and interfacing with the pulse oximeter; aprocessor mounted on the circuit board for processing at least one inputsignal according to instructions; and a memory storing a computerprogram, wherein the memory is operably coupled to the processor, andwherein the computer program includes instructions for implementing amethod of removing motion artifacts from the attenuated light signals,the method comprising: acquiring a segment of red data and a segment ofIR data from the attenuated light signals to obtain the electricalsignals representative of the attenuated light signals; conditioning theelectrical signals to reduce spectral leakage; transforming theelectrical signals into frequency domain data; identifying a pluralityof candidate peaks from the frequency domain data, wherein theidentifying includes eliminating harmonic frequencies from the pluralityof candidate peaks such that no two of the plurality of candidate peakscomprise harmonics of one another; narrow band pass filtering at each ofthe plurality of candidate peaks; developing parameters associated witheach of the plurality of candidate peaks; analyzing each of theplurality of candidate peaks with respect to at least some of thedeveloped parameters; arbitrating between at least some of the pluralityof candidate peaks employing at least some of the developed parametersto select a best frequency; and repeating the above steps with a newsegment of data.
 54. The pulse oximeter of claim 53, wherein theprocessor is a digital signal processor.
 55. A pulse oximetry system forremoving motion-induced noise artifacts from electrical signalsrepresentative of attenuated light signals comprising an input device,an output device, and motion artifact circuitry, wherein the motionartifact circuitry includes: a processor for processing at least oneinput signal according to instructions; and a memory operably coupled tothe processor storing a computer program, wherein the computer programincludes instructions for implementing a method of removing motionartifacts from the attenuated light signals, wherein the methodcomprises: acquiring a segment of red data and a segment of IR data fromthe attenuated light signals to obtain the electrical signalsrepresentative of the attenuated light signals; conditioning theelectrical signals to reduce spectral leakage; transforming theelectrical signals into frequency domain data; identifying a pluralityof candidate peaks from the frequency domain data, wherein theidentifying includes eliminating harmonic frequencies from the pluralityof candidate peaks such that no two of the plurality of candidate peakscomprise harmonics of one another; narrow band pass filtering at each ofthe plurality of candidate peaks; developing parameters associated witheach of the plurality of candidate peaks; analyzing each of theplurality of candidate peaks with respect to at least some of thedeveloped parameters; arbitrating between at least some of the pluralityof candidate peaks using at least some of the developed parameters toselect a best frequency; outputting pulse rate and saturation relatingto the best frequency; and repeating the above steps for new segments ofdata.
 56. A circuit card for use in a pulse oximetry system to removemotion-induced noise artifacts from attenuated light signals, thecircuit card comprising: a circuit board for mounting electroniccircuitry and interfacing with the pulse oximetry system; a processormounted on the circuit board for processing input signals according toinstructions; and a memory storing a computer program, wherein thememory is operably coupled to the processor, and wherein the computerprogram includes instructions for implementing a method of removingmotion artifacts from the attenuated light signals, the methodcomprising: acquiring a segment of red data and a segment of IR datafrom the attenuated light signals to obtain electrical signalsrepresentative of the attenuated light signals; conditioning theelectrical signals to reduce spectral leakage; transforming theelectrical signals into frequency domain data; identifying a pluralityof candidate peaks from the frequency domain data; narrow band passfiltering at each of the plurality of candidate peaks; developingparameters associated with each of the plurality of candidate peaks,wherein the developed parameters include window pulse rate calculated bydividing a sum of all pulse width times of all peaks in a data segmentby quantity of peaks detected in the data segment; analyzing each of theplurality of candidate peaks with respect to at least some of thedeveloped parameters; arbitrating between at least some of the pluralityof candidate peaks employing at least some of the developed parametersto select a best frequency; and repeating the above steps with a newsegment of data.
 57. The circuit card of claim 56, wherein the processoris a digital signal processor.
 58. The circuit card of claim 56, furtherconfigured for calculating pulsatile blood oxygen concentration, SpO₂,using the best frequency.
 59. The circuit card of claim 56, furtherconfigured for calculating pulse rate using the best frequency.
 60. Apulse oximeter for removing motion-induced noise artifacts fromelectrical signals representative of attenuated light signals comprisingan input device, an output device, and a motion artifact circuit card,wherein the motion artifact circuit card comprises: a circuit board formounting electronic circuitry and interfacing with the pulse oximeter; aprocessor mounted on the circuit board for processing at least one inputsignal according to instructions; and a memory storing a computerprogram, wherein the memory is operably coupled to the processor, andwherein the computer program includes instructions for implementing amethod of removing motion artifacts from the attenuated light signals,the method comprising: acquiring a segment of red data and a segment ofIR data from the attenuated light signals to obtain the electricalsignals representative of the attenuated light signals; conditioning theelectrical signals to reduce spectral leakage; transforming theelectrical signals into frequency domain data; identifying a pluralityof candidate peaks from the frequency domain data; narrow band passfiltering at each of the plurality of candidate peaks; developingparameters associated with each of the plurality of candidate peaks,wherein the developed parameters include window pulse rate calculated bydividing a sum of all pulse width times of all peaks in a data segmentby quantity of peaks detected in the data segment; analyzing each of theplurality of candidate peaks with respect to at least some of thedeveloped parameters; arbitrating between at least some of the pluralityof candidate peaks employing at least some of the developed parametersto select a best frequency; and repeating the above steps with a newsegment of data.
 61. The pulse oximeter of claim 60, wherein theprocessor is a digital signal processor.
 62. A pulse oximetry system forremoving motion-induced noise artifacts from electrical signalsrepresentative of attenuated light signals comprising an input device,an output device, and motion artifact circuitry, wherein the motionartifact circuitry includes: a processor for processing at least oneinput signal according to instructions; and a memory operably coupled tothe processor storing a computer program, wherein the computer programincludes instructions for implementing a method of removing motionartifacts from the attenuated light signals, wherein the methodcomprises: acquiring a segment of red data and a segment of IR data fromthe attenuated light signals to obtain the electrical signalsrepresentative of the attenuated light signals; conditioning theelectrical signals to reduce spectral leakage; transforming theelectrical signals into frequency domain data; identifying a pluralityof candidate peaks from the frequency domain data; narrow band passfiltering at each of the plurality of candidate peaks; developingparameters associated with each of the plurality of candidate peaks,wherein the developed parameters include window pulse rate calculated bydividing a sum of all pulse width times of all peaks in a data segmentby quantity of peaks detected in the data segment; analyzing each of theplurality of candidate peaks with respect to at least some of thedeveloped parameters; arbitrating between at least some of the pluralityof candidate peaks using at least some of the developed parameters toselect a best frequency; outputting pulse rate and saturation relatingto the best frequency; and repeating the above steps for new segments ofdata.
 63. A method of removing motion artifacts from electrical signalsrepresentative of attenuated light signals, comprising: transforming theelectrical signals into frequency domain data; identifying a pluralityof candidate peaks from the frequency domain data; narrow band passfiltering at each of the plurality of candidate peaks; developingparameters associated with each of the plurality of candidate peaks,wherein the developed parameters include window pulse rate calculated bydividing a sum of all pulse width times of all peaks in a data segmentby quantity of peaks detected in the data segment; analyzing each of theplurality of candidate peaks with respect to at least some of thedeveloped parameters; and arbitrating between at least some of theplurality of candidate peaks employing at least some of the developedparameters to select a best frequency.